def read_block(self, lazy=False, cascade=True, **kargs):
'''
Reads a block from the simple spike data file "fname" generated
with BrainWare
'''
# there are no keyargs implemented to so far. If someone tries to pass
# them they are expecting them to do something or making a mistake,
# neither of which should pass silently
if kargs:
raise NotImplementedError('This method does not have any '
'argument implemented yet')
self._fsrc = None
self.__lazy = lazy
self._blk = Block(file_origin=self._filename)
block = self._blk
# if we aren't doing cascade, don't load anything
if not cascade:
return block
# create the objects to store other objects
rcg = RecordingChannelGroup(file_origin=self._filename)
rcg.channel_indexes = np.array([], dtype=np.int)
rcg.channel_names = np.array([], dtype='S')
self.__unit = Unit(file_origin=self._filename)
# load objects into their containers
block.recordingchannelgroups.append(rcg)
rcg.units.append(self.__unit)
# initialize values
self.__t_stop = None
self.__params = None
self.__seg = None
self.__spiketimes = None
# open the file
with open(self._path, 'rb') as self._fsrc:
res = True
# while the file is not done keep reading segments
while res:
res = self.__read_id()
create_many_to_one_relationship(block)
# cleanup attributes
self._fsrc = None
self.__lazy = False
self._blk = None
self.__t_stop = None
self.__params = None
self.__seg = None
self.__spiketimes = None
return block
def proc_dam(filename):
'''Load an dam file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareDamIO to
make sure BrainwareDamIO is working properly
block = proc_dam(filename)
filename: The file name of the numpy file to load. It should end with
'*_dam_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.dam', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_dam_py2.npz'
dam file name = 'file1.dam'
'''
with np.load(filename) as damobj:
damfile = damobj.items()[0][1].flatten()
filename = os.path.basename(filename[:-12]+'.dam')
signals = [res.flatten() for res in damfile['signal']]
stimIndexes = [int(res[0, 0].tolist()) for res in damfile['stimIndex']]
timestamps = [res[0, 0] for res in damfile['timestamp']]
block = Block(file_origin=filename)
rcg = RecordingChannelGroup(file_origin=filename)
chan = RecordingChannel(file_origin=filename, index=0, name='Chan1')
rcg.channel_indexes = np.array([1])
rcg.channel_names = np.array(['Chan1'], dtype='S')
block.recordingchannelgroups.append(rcg)
rcg.recordingchannels.append(chan)
params = [res['params'][0, 0].flatten() for res in damfile['stim']]
values = [res['values'][0, 0].flatten() for res in damfile['stim']]
params = [[res1[0] for res1 in res] for res in params]
values = [[res1 for res1 in res] for res in values]
stims = [dict(zip(param, value)) for param, value in zip(params, values)]
fulldam = zip(stimIndexes, timestamps, signals, stims)
for stimIndex, timestamp, signal, stim in fulldam:
sig = AnalogSignal(signal=signal*pq.mV,
t_start=timestamp*pq.d,
file_origin=filename,
sampling_period=1.*pq.s)
segment = Segment(file_origin=filename,
index=stimIndex,
**stim)
segment.analogsignals = [sig]
block.segments.append(segment)
create_many_to_one_relationship(block)
return block
def proc_dam(filename):
'''Load an dam file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareDamIO to
make sure BrainwareDamIO is working properly
block = proc_dam(filename)
filename: The file name of the numpy file to load. It should end with
'*_dam_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.dam', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_dam_py2.npz'
dam file name = 'file1.dam'
'''
with np.load(filename) as damobj:
damfile = damobj.items()[0][1].flatten()
filename = os.path.basename(filename[:-12] + '.dam')
signals = [res.flatten() for res in damfile['signal']]
stimIndexes = [int(res[0, 0].tolist()) for res in damfile['stimIndex']]
timestamps = [res[0, 0] for res in damfile['timestamp']]
block = Block(file_origin=filename)
rcg = RecordingChannelGroup(file_origin=filename)
chan = RecordingChannel(file_origin=filename, index=0, name='Chan1')
rcg.channel_indexes = np.array([1])
rcg.channel_names = np.array(['Chan1'], dtype='S')
block.recordingchannelgroups.append(rcg)
rcg.recordingchannels.append(chan)
params = [res['params'][0, 0].flatten() for res in damfile['stim']]
values = [res['values'][0, 0].flatten() for res in damfile['stim']]
params = [[res1[0] for res1 in res] for res in params]
values = [[res1 for res1 in res] for res in values]
stims = [dict(zip(param, value)) for param, value in zip(params, values)]
fulldam = zip(stimIndexes, timestamps, signals, stims)
for stimIndex, timestamp, signal, stim in fulldam:
sig = AnalogSignal(signal=signal * pq.mV,
t_start=timestamp * pq.d,
file_origin=filename,
sampling_period=1. * pq.s)
segment = Segment(file_origin=filename, index=stimIndex, **stim)
segment.analogsignals = [sig]
block.segments.append(segment)
create_many_to_one_relationship(block)
return block
def read_block(self,
lazy = False,
cascade = True,
):
"""
"""
tree = ElementTree.parse(self.filename)
root = tree.getroot()
acq = root.find('acquisitionSystem')
nbits = int(acq.find('nBits').text)
nbchannel = int(acq.find('nChannels').text)
sampling_rate = float(acq.find('samplingRate').text)*pq.Hz
voltage_range = float(acq.find('voltageRange').text)
#offset = int(acq.find('offset').text)
amplification = float(acq.find('amplification').text)
bl = Block(file_origin = os.path.basename(self.filename).replace('.xml', ''))
if cascade:
seg = Segment()
bl.segments.append(seg)
# RC and RCG
rc_list = [ ]
for i, xml_rcg in enumerate(root.find('anatomicalDescription').find('channelGroups').findall('group')):
rcg = RecordingChannelGroup(name = 'Group {0}'.format(i))
bl.recordingchannelgroups.append(rcg)
for xml_rc in xml_rcg:
rc = RecordingChannel(index = int(xml_rc.text))
rc_list.append(rc)
rcg.recordingchannels.append(rc)
rc.recordingchannelgroups.append(rcg)
rcg.channel_indexes = np.array([rc.index for rc in rcg.recordingchannels], dtype = int)
rcg.channel_names = np.array(['Channel{0}'.format(rc.index) for rc in rcg.recordingchannels], dtype = 'S')
# AnalogSignals
reader = RawBinarySignalIO(filename = self.filename.replace('.xml', '.dat'))
seg2 = reader.read_segment(cascade = True, lazy = lazy,
sampling_rate = sampling_rate,
t_start = 0.*pq.s,
unit = pq.V, nbchannel = nbchannel,
bytesoffset = 0,
dtype = np.int16 if nbits<=16 else np.int32,
rangemin = -voltage_range/2.,
rangemax = voltage_range/2.,)
for s, sig in enumerate(seg2.analogsignals):
if not lazy:
sig /= amplification
sig.segment = seg
seg.analogsignals.append(sig)
rc_list[s].analogsignals.append(sig)
bl.create_many_to_one_relationship()
return bl
def read_block(self, lazy=False, cascade=True, **kargs):
'''
Reads a block from the raw data file "fname" generated
with BrainWare
'''
# there are no keyargs implemented to so far. If someone tries to pass
# them they are expecting them to do something or making a mistake,
# neither of which should pass silently
if kargs:
raise NotImplementedError('This method does not have any '
'argument implemented yet')
self._fsrc = None
block = Block(file_origin=self._filename)
# if we aren't doing cascade, don't load anything
if not cascade:
return block
# create the objects to store other objects
rcg = RecordingChannelGroup(file_origin=self._filename)
rchan = RecordingChannel(file_origin=self._filename,
index=1,
name='Chan1')
# load objects into their containers
rcg.recordingchannels.append(rchan)
block.recordingchannelgroups.append(rcg)
rcg.channel_indexes = np.array([1])
rcg.channel_names = np.array(['Chan1'], dtype='S')
# open the file
with open(self._path, 'rb') as fobject:
# while the file is not done keep reading segments
while True:
seg = self._read_segment(fobject, lazy)
# if there are no more Segments, stop
if not seg:
break
# store the segment and signals
block.segments.append(seg)
rchan.analogsignals.append(seg.analogsignals[0])
# remove the file object
self._fsrc = None
create_many_to_one_relationship(block)
return block
def read_block(self, lazy=False, cascade=True, **kargs):
'''
Reads a block from the raw data file "fname" generated
with BrainWare
'''
# there are no keyargs implemented to so far. If someone tries to pass
# them they are expecting them to do something or making a mistake,
# neither of which should pass silently
if kargs:
raise NotImplementedError('This method does not have any '
'argument implemented yet')
self._fsrc = None
block = Block(file_origin=self._filename)
# if we aren't doing cascade, don't load anything
if not cascade:
return block
# create the objects to store other objects
rcg = RecordingChannelGroup(file_origin=self._filename)
rchan = RecordingChannel(file_origin=self._filename,
index=1, name='Chan1')
# load objects into their containers
rcg.recordingchannels.append(rchan)
block.recordingchannelgroups.append(rcg)
rcg.channel_indexes = np.array([1])
rcg.channel_names = np.array(['Chan1'], dtype='S')
# open the file
with open(self._path, 'rb') as fobject:
# while the file is not done keep reading segments
while True:
seg = self._read_segment(fobject, lazy)
# if there are no more Segments, stop
if not seg:
break
# store the segment and signals
block.segments.append(seg)
rchan.analogsignals.append(seg.analogsignals[0])
# remove the file object
self._fsrc = None
create_many_to_one_relationship(block)
return block
def proc_f32(filename):
"""Load an f32 file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareF32IO to
make sure BrainwareF32IO is working properly
block = proc_f32(filename)
filename: The file name of the numpy file to load. It should end with
'*_f32_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.f32', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_f32_py2.npz'
f32 file name = 'file1.f32'
"""
filenameorig = os.path.basename(filename[:-12] + ".f32")
# create the objects to store other objects
block = Block(file_origin=filenameorig)
rcg = RecordingChannelGroup(file_origin=filenameorig)
rcg.channel_indexes = np.array([], dtype=np.int)
rcg.channel_names = np.array([], dtype="S")
unit = Unit(file_origin=filenameorig)
# load objects into their containers
block.recordingchannelgroups.append(rcg)
rcg.units.append(unit)
try:
with np.load(filename) as f32obj:
f32file = f32obj.items()[0][1].flatten()
except IOError as exc:
if "as a pickle" in exc.message:
block.create_many_to_one_relationship()
return block
else:
raise
sweeplengths = [res[0, 0].tolist() for res in f32file["sweeplength"]]
stims = [res.flatten().tolist() for res in f32file["stim"]]
sweeps = [res["spikes"].flatten() for res in f32file["sweep"] if res.size]
fullf32 = zip(sweeplengths, stims, sweeps)
for sweeplength, stim, sweep in fullf32:
for trainpts in sweep:
if trainpts.size:
trainpts = trainpts.flatten().astype("float32")
else:
trainpts = []
paramnames = ["Param%s" % i for i in range(len(stim))]
params = dict(zip(paramnames, stim))
train = SpikeTrain(trainpts, units=pq.ms, t_start=0, t_stop=sweeplength, file_origin=filenameorig)
segment = Segment(file_origin=filenameorig, **params)
segment.spiketrains = [train]
unit.spiketrains.append(train)
block.segments.append(segment)
block.create_many_to_one_relationship()
return block
def proc_f32(filename):
'''Load an f32 file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareF32IO to
make sure BrainwareF32IO is working properly
block = proc_f32(filename)
filename: The file name of the numpy file to load. It should end with
'*_f32_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.f32', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_f32_py2.npz'
f32 file name = 'file1.f32'
'''
filenameorig = os.path.basename(filename[:-12] + '.f32')
# create the objects to store other objects
block = Block(file_origin=filenameorig)
rcg = RecordingChannelGroup(file_origin=filenameorig)
rcg.channel_indexes = np.array([], dtype=np.int)
rcg.channel_names = np.array([], dtype='S')
unit = Unit(file_origin=filenameorig)
# load objects into their containers
block.recordingchannelgroups.append(rcg)
rcg.units.append(unit)
try:
with np.load(filename) as f32obj:
f32file = f32obj.items()[0][1].flatten()
except IOError as exc:
if 'as a pickle' in exc.message:
create_many_to_one_relationship(block)
return block
else:
raise
sweeplengths = [res[0, 0].tolist() for res in f32file['sweeplength']]
stims = [res.flatten().tolist() for res in f32file['stim']]
sweeps = [res['spikes'].flatten() for res in f32file['sweep'] if res.size]
fullf32 = zip(sweeplengths, stims, sweeps)
for sweeplength, stim, sweep in fullf32:
for trainpts in sweep:
if trainpts.size:
trainpts = trainpts.flatten().astype('float32')
else:
trainpts = []
paramnames = ['Param%s' % i for i in range(len(stim))]
params = dict(zip(paramnames, stim))
train = SpikeTrain(trainpts,
units=pq.ms,
t_start=0,
t_stop=sweeplength,
file_origin=filenameorig)
segment = Segment(file_origin=filenameorig, **params)
segment.spiketrains = [train]
unit.spiketrains.append(train)
block.segments.append(segment)
create_many_to_one_relationship(block)
return block
def read_block(
self,
lazy=False,
cascade=True,
):
"""
"""
tree = ElementTree.parse(self.filename)
root = tree.getroot()
acq = root.find('acquisitionSystem')
nbits = int(acq.find('nBits').text)
nbchannel = int(acq.find('nChannels').text)
sampling_rate = float(acq.find('samplingRate').text) * pq.Hz
voltage_range = float(acq.find('voltageRange').text)
#offset = int(acq.find('offset').text)
amplification = float(acq.find('amplification').text)
bl = Block(
file_origin=os.path.basename(self.filename).replace('.xml', ''))
if cascade:
seg = Segment()
bl.segments.append(seg)
# RC and RCG
rc_list = []
for i, xml_rcg in enumerate(
root.find('anatomicalDescription').find(
'channelGroups').findall('group')):
rcg = RecordingChannelGroup(name='Group {}'.format(i))
bl.recordingchannelgroups.append(rcg)
for xml_rc in xml_rcg:
rc = RecordingChannel(index=int(xml_rc.text))
rc_list.append(rc)
rcg.recordingchannels.append(rc)
rc.recordingchannelgroups.append(rcg)
rcg.channel_indexes = np.array(
[rc.index for rc in rcg.recordingchannels], dtype=int)
rcg.channel_names = np.array([
'Channel{}'.format(rc.index)
for rc in rcg.recordingchannels
],
dtype='S')
# AnalogSignals
reader = RawBinarySignalIO(
filename=self.filename.replace('.xml', '.dat'))
seg2 = reader.read_segment(
cascade=True,
lazy=lazy,
sampling_rate=sampling_rate,
t_start=0. * pq.s,
unit=pq.V,
nbchannel=nbchannel,
bytesoffset=0,
dtype=np.int16 if nbits <= 16 else np.int32,
rangemin=-voltage_range / 2.,
rangemax=voltage_range / 2.,
)
for s, sig in enumerate(seg2.analogsignals):
if not lazy:
sig /= amplification
sig.segment = seg
seg.analogsignals.append(sig)
rc_list[s].analogsignals.append(sig)
create_many_to_one_relationship(bl)
return bl
